Ontology Research Group Overview

Transcription

1 Ontology Research Group Overview ORG Dr. Valerie Cross Sriram Ramakrishnan Ramanathan Somasundaram En Yu Yi Sun Miami University OCWIC 2007 February 17, Deer Creek Resort OCWIC

2 Outline Motivation for Research Agenda Semantic Web What are Ontologies? Some Ontologies Used in our Research Overview of Current Ontology Research Areas Semantic Similarity in Ontologies Ontology Evaluation Ontology Learning Ontology Querying Ontology 3D Visualization Possibilities OCWIC

3 Building the Semantic Web Scientific America, May OCWIC

4 Ian Horrocks, ICCL Summer School Course 2005 OCWIC

5 he Semantic Web is an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. T. Berners-Lee, J. Hendler, O. Lassila OCWIC

6 Ian Horrocks, ICCL Summer School Course 2005 OCWIC

7 Ontology Dimensions [Welty, Uschold, Gruninger, Lehmann & McGuinness, 1999] OCWIC

8 Ian Horrocks, ICCL Summer School Course 2005 OCWIC

9 Current Ontologies Used in Our Research WordNet UMLS Gene Ontology SEURAT s Argument Ontology PSCS Ontologies UNSPSC ecl@ass OCWIC

10 WordNet Is a Hierarchy Four parts of speech nouns, verbs, adjectives and adverbs. Concept Node = Synonyms + Definition (gloss) ~ 111,400 concepts. ~ 13 types of relationships. 9 noun is a hierarchies with an average depth of verb is a hierarchies with an average depth of 2. other roots for noun hierarchies: psychological_feature, abstraction, state, event, act, possession, group, phenomenon synonymy relation implicit in each node (through synsets) hyponymy (IS-A) relation, its inverse, hypernymy; six meronymic (PART-OF) relations (and inverses): COMPONENT-OF, MEMBER-OF SUBSTANCE-OF antonymy, the COMPLEMENT-OF relation. OCWIC

11 UMLS Semantic Network OCWIC

12 Gene Ontology - BP GO contains three structured, controlled vocabularies describing gene products biological processes (BP), cellular components (C molecular functions (M in a species-independent manner. OCWIC

13 Semantic Distance, Similarity, Relatedness Semantic relatedness General term involving many relationships car-wheel (meronymy) hot-cold (antonymy) pencil-paper (functional) penguin-antarctica (association) vehicle-car (subsumption) Semantic similarity More specific term involving likeness bank-trust company (synonymy) Distance Inverse of either one reldist(x)=semantic relatedness -1 (x) simdist(x)=semantic similarity -1 (x) OCWIC

14 Applications of Semantic Relatedness Interoperability between Ontologies Merging, Linking, agent communication Information Retrieval To improve automated assignment of indexing based descriptors Semantic Vocabulary Integration Choose closest related concepts while translating in and out of the multiple vocabularies/ontologies Semantic Vocabulary Interoperability Project at OCWIC

15 Semantic Similarity Research (Youbo Wang) Comparison of semantic similarity measures in literature using experimental approach for different domain ontologies and types of ontologies Create a testbed Acquire different domain ontologies and different types of ontologies (WordNet and UMLS) Develop plug-ins for Protégé for all the existing semantic relatedness measures Develop comparison with human judgment for new domain ontologies such as medical OCWIC

16 Motivation for Ontology Evaluation Ontologies the backbone of the Semantic Web Development and deployment of ontology-based software solutions requires considerable time and effort Numerous existing ontologies in libraries available on the WWW Why reinvent the wheel? Reuse of ontologies important to SW success OCWIC

17 What is ontology evaluation? Ontology evaluation - key problem in the field of ontology development and reuse. Selection vs. Evaluation Two separate tasks? How related? When does it occur? Selection Evaluation? Evaluation Selection? Ontology Selection: Ontology Evaluation on the Real Semantic Web (Sabou, Lopez, Motta,Uren EON 2006) OCWIC

18 Ontology Consumer Analysis Tool Objective is to examine from Consumer perspective to Reuse or adapt ontology Suite of metrics from conceptual modeling, software development, information systems development information retrieval Used on WordNet, UMLS, UNSPSC and OCWIC

19 OntoCAT (Anindita Pal) plug-in for OWL Protégé very parameterized Intensional (classes) and extensional (instances) View metrics interested in Size Structure User selectable root for analysis Hub concept analysis User selectable relation for establishing extensional structure OCWIC

20 OntoCAT intensional metrics UNSPSC RDF/RDF(S) version of UNSPSC is developed by Michel Klein and was obtained from OCWIC

21 Ontology Learning (Vishal Bathija) Objective: Adapt the SEURAT Argument Ontology from the Software Engineering Domain to the Spacecraft Engineering Domain. Developed an Ontology Adaptation Methodology Pruning: Remove from the existing Argument Ontology concepts not relevant to Engineering Design (ED) by analyzing text corpuses specific to domain Adapting and Specializing: Adding new concepts and relations (is-a only) to transform pruned ontology into the ED criteria ontology. Evaluation Evaluate the adapted ontology based on performance measures used in ontology learning research literature OCWIC

22 Ontology Adaptation Software Architecture Input Resources: Argument Ontology Training Corpus Test Corpus General Corpus Software Resources: Apache Lucene JWordNet Test Corpus General Corpus OCWIC

23 QUOTA Ontology Querying (Yi Sun) QUerying with Ontological Terminologies and their Annotations uses previous research on: Semantic Similarity Ontology metrics Gene Ontology Annotations from model organism databases. OCWIC

24 Ontology 3D Visualization (Ramanathan Somasundaram) Objective: build a package of 3D visualization algorithms to produce visualizations of different types of ontologies for different purposes Experiment with different weight functions Apply the weight functions to different force directed layout algorithms Designing filtering view for different tasks Parallelizing the execution OCWIC

25 Visualization using weight function Interval rank (SpindleViz) 1; 5 B; 3 C; 3 I; 1 G; 0 J; 0 F; -1 E;-1 A;-2 D;-3 K; F; 1 B; 7 1; 11 I; 3 C; 5 G; 2 E; 2 J; 2 A; 1 D; 1 K; ;-5 Balanced Height Weight Function, W BH (C) -5 0; 0 extent (c) Weight Function, W NS (C) 0 OCWIC

26 Research Possibilities. Semantic Similarity Develop a task/application-specific evaluation procedure Continue with mathematical analysis and properties Apply to 3D visualziation Investigate semantic relatedness measures in assessing ontology quality OCWIC

27 Research Possibilities Ontology Evaluation Interface with candidate selection approaches before perform detailed analysis Comparison metrics/charts/visualization for multiple ontologies for candidates Visualization to help consumers see ontology for reuse and comparison Hubs visualization Improvement Individual hub visualization Top-level summary / visualization Bottom-up level summary / visualization Combine and aggregate analysis results to provide consumers with summaries characterizing each ontology and a vocabulary for critiquing an ontology after all the metric analysis OCWIC

28 Research Possibilities Ontology Learning More experimentation with OntoLT user-specified rules to produce concept trees Use of DR sublanguage to parse the training text collections looking for keywords of the domain sublanguage Limitation of Ontology adaptation software Dependency on SCHUG Find other open source software or develop software to provide the SCHUG functionality Domain expert user interface to provide input to the pruning and merging process OCWIC

29 Interested? Send me Be happy to send some references Consider applying to Miami Unversity s masters program in computer science Most graduate students fully funded as a first year teaching assistant and a second year research assistant Pick up Miami folder at OCWIC if you don t have one. OCWIC

30 OCWIC